Integrative multi-omics profiling in human decedents receiving pig heart xenografts.

Journal

Nature medicine

ISSN: 1546-170X

Titre abrégé: Nat Med

Pays: United States

ID NLM: 9502015

Informations de publication

Date de publication:
17 May 2024

Historique:

received: 31 08 2023

accepted: 03 04 2024

medline: 18 5 2024

pubmed: 18 5 2024

entrez: 17 5 2024

Statut: aheadofprint

Résumé

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.

Identifiants

DOI: 10.1038/s41591-024-02972-1 PMID: 38760586

pubmed: 38760586

doi: 10.1038/s41591-024-02972-1

pii: 10.1038/s41591-024-02972-1

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Subventions

Organisme : NIAID NIH HHS

ID : R01 AI144522

Pays : United States

Organisme : NIAID NIH HHS

ID : R01 AI144522

Pays : United States

Informations de copyright

Références

Cooper, D. K. C. & Pierson, R. N. Milestones on the path to clinical pig organ xenotransplantation. Am. J. Transpl. 23, 326–335 (2023).

doi: 10.1016/j.ajt.2022.12.023

Wolbrom, D. H., Kim, J. I. & Griesemer, A. The road to xenotransplantation. Curr. Opin. Organ Transpl. 28, 65–70 (2023).

doi: 10.1097/MOT.0000000000001055

Ekser, B., Rigotti, P., Gridelli, B. & Cooper, D. K. C. Xenotransplantation of solid organs in the pig-to-primate model. Transpl. Immunol. 21, 87–92 (2009).

pubmed: 18955143 doi: 10.1016/j.trim.2008.10.005

Pintore, L. et al. Clinicopathological findings in non-human primate recipients of porcine renal xenografts: quantitative and qualitative evaluation of proteinuria. Xenotransplantation 20, 449–457 (2013).

pubmed: 24112104 doi: 10.1111/xen.12063

Mohiuddin, M. M. et al. Graft dysfunction in compassionate use of genetically engineered pig-to-human cardiac xenotransplantation: a case report. Lancet 402, 397–410 (2023).

pubmed: 37393920 doi: 10.1016/S0140-6736(23)00775-4

Moazami, N. et al. Pig-to-human heart xenotransplantation in two recently deceased human recipients. Nat. Med. 29, 1989–1997 (2023).

pubmed: 37488288 doi: 10.1038/s41591-023-02471-9

Zhou, W. et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature 569, 663–671 (2019).

pubmed: 31142858 pmcid: 6666404 doi: 10.1038/s41586-019-1236-x

Garrett-Bakelman, F. E. et al. The NASA Twins Study: a multidimensional analysis of a year-long human spaceflight. Science 364, eaau8650 (2019).

pubmed: 30975860 pmcid: 7580864 doi: 10.1126/science.aau8650

Piening, B. D. et al. Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. 6, 157–170 (2018).

pubmed: 29361466 pmcid: 6021558 doi: 10.1016/j.cels.2017.12.013

Schüssler-Fiorenza Rose, S. M. et al. A longitudinal big data approach for precision health. Nat. Med. 25, 792–804 (2019).

pubmed: 31068711 doi: 10.1038/s41591-019-0414-6

Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103 (2019).

pubmed: 30561547 doi: 10.1093/bioinformatics/bty537

Shaked, A. et al. An ectopically expressed serum miRNA signature is prognostic, diagnostic, and biologically related to liver allograft rejection. Hepatology 65, 269–280 (2017).

pubmed: 27533743 doi: 10.1002/hep.28786

Piening, B. D. et al. Whole transcriptome profiling of prospective endomyocardial biopsies reveals prognostic and diagnostic signatures of cardiac allograft rejection. J. Heart Lung Transpl. 41, 840–848 (2022).

doi: 10.1016/j.healun.2022.01.1377

Long, X., Yuan, X. & Du, J. Single-cell and spatial transcriptomics: advances in heart development and disease applications. Comput Struct. Biotechnol. J. 21, 2717–2731 (2023).

pubmed: 37181659 pmcid: 10173363 doi: 10.1016/j.csbj.2023.04.007

Ospina, O., Soupir, A. & Fridley, B. L. A primer on preprocessing, visualization, clustering, and phenotyping of barcode-based spatial transcriptomics data. Methods Mol. Biol. 2629, 115–140 (2023).

pubmed: 36929076 doi: 10.1007/978-1-0716-2986-4_7

Finn, E. H. & Misteli, T. Molecular basis and biological function of variability in spatial genome organization. Science 365, eaaw9498 (2019).

pubmed: 31488662 pmcid: 7421438 doi: 10.1126/science.aaw9498

Martin, M. D. & Badovinac, V. P. Defining memory CD8 T cell. Front Immunol. 9, 2692 (2018).

pubmed: 30515169 pmcid: 6255921 doi: 10.3389/fimmu.2018.02692

Golubovskaya, V. & Wu, L. Different subsets of T cells, memory, effector functions, and CAR-T immunotherapy. Cancers 8, 36 (2016).

pubmed: 26999211 pmcid: 4810120 doi: 10.3390/cancers8030036

Brinas, F., Danger, R. & Brouard, S. TCL1A, B cell regulation and tolerance in renal transplantation. Cells 10, 1367 (2021).

pubmed: 34206047 pmcid: 8230170 doi: 10.3390/cells10061367

Andrijevic, D. et al. Cellular recovery after prolonged warm ischaemia of the whole body. Nature 608, 405–412 (2022).

pubmed: 35922506 pmcid: 9518831 doi: 10.1038/s41586-022-05016-1

Sathaliyawala, T. et al. Distribution and compartmentalization of human circulating and tissue-resident memory T cell subsets. Immunity 38, 187–197 (2013).

pubmed: 23260195 doi: 10.1016/j.immuni.2012.09.020

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

pubmed: 25516281 pmcid: 4302049 doi: 10.1186/s13059-014-0550-8

Roh, J. S. & Sohn, D. H. Damage-associated molecular patterns in inflammatory diseases. Immune Netw. 18, e27 (2018).

pubmed: 30181915 pmcid: 6117512 doi: 10.4110/in.2018.18.e27

Byrne, G. W., Du, Z., Sun, Z., Asmann, Y. W. & McGregor, C. G. A. Changes in cardiac gene expression after pig-to-primate orthotopic xenotransplantation. Xenotransplantation 18, 14–27 (2011).

pubmed: 21342284 pmcid: 10022692 doi: 10.1111/j.1399-3089.2010.00620.x

Linna-Kuosmanen, S. et al. Cardiovascular disease causes proinflammatory microvascular changes in the human right atrium. Preprint at bioRxiv https://doi.org/10.1101/2021.06.23.449672 (2022).

Shimizu, Y. et al. Impact of lymphangiogenesis on cardiac remodeling after ischemia and reperfusion injury. J. Am. Heart Assoc. 7, e009565 (2018).

pubmed: 30371303 pmcid: 6404883 doi: 10.1161/JAHA.118.009565

Giarraputo, A. et al. Banff human organ transplant consensus gene panel for the detection of antibody mediated rejection in heart allograft biopsies. Transpl. Int. 36, 11710 (2023).

pubmed: 37745639 pmcid: 10515212 doi: 10.3389/ti.2023.11710

Florens, N. et al. Interleukin-33 mediates cardiomyopathy after acute kidney injury by signaling to cardiomyocytes. Circulation 147, 746–758 (2023).

pubmed: 36695175 pmcid: 9992318 doi: 10.1161/CIRCULATIONAHA.122.063014

Mehrotra, P. et al. IL-17 mediates neutrophil infiltration and renal fibrosis following recovery from ischemia reperfusion: compensatory role of natural killer cells in athymic rats. Am. J. Physiol. Ren. Physiol. 312, F385–F397 (2017).

doi: 10.1152/ajprenal.00462.2016

Baban, B., Liu, J. Y. & Mozaffari, M. S. SGK-1 regulates inflammation and cell death in the ischemic-reperfused heart: pressure-related effects. Am. J. Hypertens. 27, 846–856 (2014).

doi: 10.1093/ajh/hpt269

Azad, A. K., Chakrabarti, S., Xu, Z., Davidge, S. T. & Fu, Y. Coiled-coil domain containing 3 (CCDC3) represses tumor necrosis factor-α/nuclear factor κB-induced endothelial inflammation. Cell Signal 26, 2793–2800 (2014).

pubmed: 25193116 doi: 10.1016/j.cellsig.2014.08.025

Flinn, M. A. et al. Myofibroblast Ccn3 is regulated by Yap and Wwtr1 and contributes to adverse cardiac outcomes. Front. Cardiovasc. Med. 10, 1142612 (2023).

pubmed: 36998974 pmcid: 10043314 doi: 10.3389/fcvm.2023.1142612

Banaszkiewicz, M., Olejnik, A., Krzywonos-Zawadzka, A., Hałucha, K. & Bil-Lula, I. Expression of atrial‑fetal light chains in cultured human cardiomyocytes after chemical ischemia‑reperfusion injury. Mol. Med. Rep. 24, 770 (2021).

pubmed: 34490485 pmcid: 8430302 doi: 10.3892/mmr.2021.12410

Byrne, G. W. & McGregor, C. G. A. Cardiac xenotransplantation: progress and challenges. Curr. Opin. Organ Transpl. 17, 148–154 (2012).

doi: 10.1097/MOT.0b013e3283509120

Koritzinsky, E. H., Tsuda, H. & Fairchild, R. L. Endogenous memory T cells with donor-reactivity: early post-transplant mediators of acute graft injury in unsensitized recipients. Transpl. Int. 34, 1360–1373 (2021).

pubmed: 33963616 doi: 10.1111/tri.13900

Pearl, J. P. et al. Immunocompetent T-cells with a memory-like phenotype are the dominant cell type following antibody-mediated T-cell depletion. Am. J. Transpl. 5, 465–474 (2005).

doi: 10.1111/j.1600-6143.2005.00759.x

White, J. T., Cross, E. W. & Kedl, R. M. Antigen-inexperienced memory CD8+ T cells: where they come from and why we need them. Nat. Rev. Immunol. 17, 391–400 (2017).

pubmed: 28480897 pmcid: 5569888 doi: 10.1038/nri.2017.34

Nakamura, K., Kageyama, S. & Kupiec-Weglinski, J. W. Innate immunity in ischemia-reperfusion injury and graft rejection. Curr. Opin. Organ Transpl. 24, 687–693 (2019).

doi: 10.1097/MOT.0000000000000709

El-Sawy, T., Miura, M. & Fairchild, R. Early T cell response to allografts occurring prior to alloantigen priming up-regulates innate-mediated inflammation and graft necrosis. Am. J. Pathol. 165, 147–157 (2004).

pubmed: 15215170 pmcid: 1618530 doi: 10.1016/S0002-9440(10)63283-X

Mohiuddin, M. M. et al. Chimeric 2C10R4 anti-CD40 antibody therapy is critical for long-term survival of GTKO.hCD46.hTBM pig-to-primate cardiac xenograft. Nat. Commun. 7, 11138 (2016).

pubmed: 27045379 pmcid: 4822024 doi: 10.1038/ncomms11138

Shewmon, D. A. Chronic ‘brain death’: meta-analysis and conceptual consequences. Neurology 51, 1538–1545 (1998).

pubmed: 9855499 doi: 10.1212/WNL.51.6.1538

Cooper, D. K. C. et al. Alpha1,3-galactosyltransferase gene-knockout pigs for xenotransplantation: where do we go from here? Transplantation 84, 1–7 (2007).

pubmed: 17627227 doi: 10.1097/01.tp.0000260427.75804.f2

Schroder, P. M. et al. The past, present, and future of costimulation blockade in organ transplantation. Curr. Opin. Organ Transpl. 24, 391–401 (2019).

doi: 10.1097/MOT.0000000000000656

ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

doi: 10.1038/nature11247

Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

pubmed: 29409532 pmcid: 5802054 doi: 10.1186/s13059-017-1382-0

Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

pubmed: 31740819 pmcid: 6884693 doi: 10.1038/s41592-019-0619-0

McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).

Traag, V., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

pubmed: 30914743 pmcid: 6435756 doi: 10.1038/s41598-019-41695-z

Badia-I-Mompel, P. et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinform. Adv. 2, vbac016 (2022).

pubmed: 36699385 pmcid: 9710656 doi: 10.1093/bioadv/vbac016

La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

pubmed: 30089906 pmcid: 6130801 doi: 10.1038/s41586-018-0414-6

Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

pubmed: 30954476 pmcid: 6625319 doi: 10.1016/j.cels.2018.11.005

Fang, Z., Liu, X. & Peltz, G. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics 39, btac757 (2023).

pubmed: 36426870 doi: 10.1093/bioinformatics/btac757

Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

pubmed: 33597522 pmcid: 7889871 doi: 10.1038/s41467-021-21246-9

Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).

pubmed: 35027729 doi: 10.1038/s41587-021-01139-4

Lopez, R. et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat. Biotechnol. 40, 1360–1369 (2022).

pubmed: 35449415 pmcid: 9756396 doi: 10.1038/s41587-022-01272-8

Contrepois, K., Jiang, L. & Snyder, M. Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-Mass spectrometry. Mol. Cell Proteom. 14, 1684–1695 (2015).

doi: 10.1074/mcp.M114.046508

Contrepois, K. et al. Cross-platform comparison of untargeted and targeted lipidomics approaches on aging mouse plasma. Sci. Rep. 8, 17747 (2018).

pubmed: 30532037 pmcid: 6288111 doi: 10.1038/s41598-018-35807-4

Su, B. et al. A DMS shotgun lipidomics workflow application to facilitate high-throughput, comprehensive lipidomics. J. Am. Soc. Mass. Spectrom. 32, 2655–2663 (2021).

pubmed: 34637296 pmcid: 8985811 doi: 10.1021/jasms.1c00203

Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007

Cannon, R. L., Dave, J. V. & Bezdek, J. C. Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 8, 248–255 (1986).

pubmed: 21869343 doi: 10.1109/TPAMI.1986.4767778

Schmauch, E. et al. Datasets: integrative multi-omics profiling in human decedents receiving pig heart xenografts. Zenodo https://doi.org/10.5281/zenodo.10888596 (2024).

Schmauch, E. et al. Scripts for integrative multi-omics profiling in human decedents receiving pig heart xenografts. Zenodo https://doi.org/10.5281/zenodo.10887629 (2024).